Overview

Dataset statistics

Number of variables20
Number of observations468
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory199.0 KiB
Average record size in memory435.5 B

Variable types

Numeric15
Text3
Categorical2

Alerts

PLAYER_ID is highly overall correlated with AGE_baseHigh correlation
AGE_base is highly overall correlated with PLAYER_IDHigh correlation
GP_base is highly overall correlated with L_base and 2 other fieldsHigh correlation
W_base is highly overall correlated with GP_base and 1 other fieldsHigh correlation
L_base is highly overall correlated with GP_base and 1 other fieldsHigh correlation
W_PCT_base is highly overall correlated with L_base and 3 other fieldsHigh correlation
MIN_base is highly overall correlated with FG3A and 5 other fieldsHigh correlation
FGM_base is highly overall correlated with FG3A and 4 other fieldsHigh correlation
FGA_base is highly overall correlated with FG3A and 4 other fieldsHigh correlation
FG3M is highly overall correlated with FG3A and 4 other fieldsHigh correlation
FG3A is highly overall correlated with FG3M and 5 other fieldsHigh correlation
FG3_PCT is highly overall correlated with FG3A and 1 other fieldsHigh correlation
FTM is highly overall correlated with FG3A and 3 other fieldsHigh correlation
TEAM_ID_base is highly overall correlated with TEAM_ABBREVIATION_adv and 1 other fieldsHigh correlation
FG_PCT_base is highly overall correlated with FG3_PCTHigh correlation
TEAM_ABBREVIATION_base is highly overall correlated with TEAM_ABBREVIATION_adv and 2 other fieldsHigh correlation
TEAM_ABBREVIATION_adv is highly overall correlated with TEAM_ABBREVIATION_base and 2 other fieldsHigh correlation
TEAM_ID_base is uniformly distributedUniform
TEAM_ABBREVIATION_base is uniformly distributedUniform
TEAM_ABBREVIATION_adv is uniformly distributedUniform
PLAYER_ID has unique valuesUnique
PLAYER_NAME has unique valuesUnique
FG3M has 46 (9.8%) zerosZeros
FG3A has 24 (5.1%) zerosZeros
FG3_PCT has 37 (7.9%) zerosZeros
FTM has 6 (1.3%) zerosZeros

Reproduction

Analysis started2025-12-12 01:33:32.835712
Analysis finished2025-12-12 01:33:44.682531
Duration11.85 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

PLAYER_ID
Real number (ℝ)

High correlation  Unique 

Distinct468
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1393503.9
Minimum2544
Maximum1642419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:44.728953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2544
5-th percentile202682.05
Q11627871
median1630165
Q31631121.8
95-th percentile1642271.6
Maximum1642419
Range1639875
Interquartile range (IQR)3250.75

Descriptive statistics

Standard deviation534862.63
Coefficient of variation (CV)0.38382571
Kurtosis1.2344472
Mean1393503.9
Median Absolute Deviation (MAD)1699.5
Skewness-1.7958911
Sum6.5215982 × 108
Variance2.8607803 × 1011
MonotonicityNot monotonic
2025-12-11T20:33:44.800282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2039321
 
0.2%
16289881
 
0.2%
16301741
 
0.2%
16305981
 
0.2%
16417371
 
0.2%
16423491
 
0.2%
16312601
 
0.2%
16423581
 
0.2%
16306391
 
0.2%
2026921
 
0.2%
Other values (458)458
97.9%
ValueCountFrequency (%)
25441
0.2%
1011081
0.2%
2007681
0.2%
2007821
0.2%
2011421
0.2%
2011431
0.2%
2011441
0.2%
2011451
0.2%
2015661
0.2%
2015671
0.2%
ValueCountFrequency (%)
16424191
0.2%
16424031
0.2%
16423821
0.2%
16423771
0.2%
16423671
0.2%
16423661
0.2%
16423591
0.2%
16423581
0.2%
16423551
0.2%
16423531
0.2%

TEAM_ID_base
Real number (ℝ)

High correlation  Uniform 

Distinct30
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6106128 × 109
Minimum1.6106127 × 109
Maximum1.6106128 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:44.853788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.6106127 × 109
5-th percentile1.6106127 × 109
Q11.6106127 × 109
median1.6106128 × 109
Q31.6106128 × 109
95-th percentile1.6106128 × 109
Maximum1.6106128 × 109
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6182789
Coefficient of variation (CV)5.3509317 × 10-9
Kurtosis-1.1889699
Mean1.6106128 × 109
Median Absolute Deviation (MAD)7
Skewness-0.010583617
Sum7.5376677 × 1011
Variance74.274731
MonotonicityNot monotonic
2025-12-11T20:33:44.896413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
161061275518
 
3.8%
161061276417
 
3.6%
161061275717
 
3.6%
161061274117
 
3.6%
161061274017
 
3.6%
161061275017
 
3.6%
161061274716
 
3.4%
161061274616
 
3.4%
161061274816
 
3.4%
161061276116
 
3.4%
Other values (20)301
64.3%
ValueCountFrequency (%)
161061273715
3.2%
161061273814
3.0%
161061273914
3.0%
161061274017
3.6%
161061274117
3.6%
161061274215
3.2%
161061274314
3.0%
161061274414
3.0%
161061274516
3.4%
161061274616
3.4%
ValueCountFrequency (%)
161061276616
3.4%
161061276515
3.2%
161061276417
3.6%
161061276316
3.4%
161061276215
3.2%
161061276116
3.4%
161061276015
3.2%
161061275915
3.2%
161061275815
3.2%
161061275717
3.6%

AGE_base
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.566239
Minimum19
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:44.948261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile21
Q123
median26
Q329
95-th percentile35
Maximum40
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4957621
Coefficient of variation (CV)0.1692284
Kurtosis0.017017101
Mean26.566239
Median Absolute Deviation (MAD)3
Skewness0.70667839
Sum12433
Variance20.211877
MonotonicityNot monotonic
2025-12-11T20:33:44.990971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2454
11.5%
2240
 
8.5%
2339
 
8.3%
2539
 
8.3%
2737
 
7.9%
2636
 
7.7%
2833
 
7.1%
2931
 
6.6%
2129
 
6.2%
3220
 
4.3%
Other values (12)110
23.5%
ValueCountFrequency (%)
195
 
1.1%
2017
 
3.6%
2129
6.2%
2240
8.5%
2339
8.3%
2454
11.5%
2539
8.3%
2636
7.7%
2737
7.9%
2833
7.1%
ValueCountFrequency (%)
404
 
0.9%
393
 
0.6%
382
 
0.4%
374
 
0.9%
367
 
1.5%
3510
2.1%
348
 
1.7%
3316
3.4%
3220
4.3%
3120
4.3%

GP_base
Real number (ℝ)

High correlation 

Distinct79
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.40812
Minimum1
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:45.043645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.35
Q139.75
median57
Q370.25
95-th percentile79
Maximum82
Range81
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation20.143226
Coefficient of variation (CV)0.37715663
Kurtosis-0.6181715
Mean53.40812
Median Absolute Deviation (MAD)15
Skewness-0.5798189
Sum24995
Variance405.74957
MonotonicityNot monotonic
2025-12-11T20:33:45.104040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6415
 
3.2%
7415
 
3.2%
5714
 
3.0%
7213
 
2.8%
6012
 
2.6%
7912
 
2.6%
7512
 
2.6%
8211
 
2.4%
7011
 
2.4%
4611
 
2.4%
Other values (69)342
73.1%
ValueCountFrequency (%)
11
 
0.2%
32
 
0.4%
41
 
0.2%
51
 
0.2%
61
 
0.2%
82
 
0.4%
93
0.6%
101
 
0.2%
122
 
0.4%
135
1.1%
ValueCountFrequency (%)
8211
2.4%
814
 
0.9%
807
1.5%
7912
2.6%
788
1.7%
778
1.7%
768
1.7%
7512
2.6%
7415
3.2%
739
1.9%

W_base
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.99359
Minimum0
Maximum64
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:45.169616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q115
median27
Q338
95-th percentile49.65
Maximum64
Range64
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.425396
Coefficient of variation (CV)0.53440078
Kurtosis-0.81610207
Mean26.99359
Median Absolute Deviation (MAD)12
Skewness0.20701286
Sum12633
Variance208.09204
MonotonicityNot monotonic
2025-12-11T20:33:45.401573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2116
 
3.4%
3016
 
3.4%
1415
 
3.2%
3814
 
3.0%
4413
 
2.8%
1713
 
2.8%
1013
 
2.8%
3613
 
2.8%
3313
 
2.8%
1112
 
2.6%
Other values (53)330
70.5%
ValueCountFrequency (%)
01
 
0.2%
13
 
0.6%
23
 
0.6%
35
1.1%
45
1.1%
53
 
0.6%
610
2.1%
710
2.1%
88
1.7%
99
1.9%
ValueCountFrequency (%)
641
 
0.2%
632
 
0.4%
621
 
0.2%
601
 
0.2%
591
 
0.2%
583
0.6%
571
 
0.2%
555
1.1%
541
 
0.2%
532
 
0.4%

L_base
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.41453
Minimum0
Maximum64
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:45.461246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q117
median26
Q335
95-th percentile48
Maximum64
Range64
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.687614
Coefficient of variation (CV)0.48032708
Kurtosis-0.3864499
Mean26.41453
Median Absolute Deviation (MAD)9
Skewness0.21052575
Sum12362
Variance160.97555
MonotonicityNot monotonic
2025-12-11T20:33:45.526255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2921
 
4.5%
2619
 
4.1%
2118
 
3.8%
3117
 
3.6%
3917
 
3.6%
1916
 
3.4%
2515
 
3.2%
3215
 
3.2%
2715
 
3.2%
1813
 
2.8%
Other values (47)302
64.5%
ValueCountFrequency (%)
01
 
0.2%
25
1.1%
35
1.1%
46
1.3%
53
 
0.6%
67
1.5%
79
1.9%
83
 
0.6%
93
 
0.6%
109
1.9%
ValueCountFrequency (%)
641
 
0.2%
591
 
0.2%
581
 
0.2%
573
0.6%
561
 
0.2%
546
1.3%
532
 
0.4%
512
 
0.4%
501
 
0.2%
494
0.9%

W_PCT_base
Real number (ℝ)

High correlation 

Distinct286
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49688889
Minimum0
Maximum1
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:45.578632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.20175
Q10.37075
median0.5
Q30.615
95-th percentile0.78665
Maximum1
Range1
Interquartile range (IQR)0.24425

Descriptive statistics

Standard deviation0.173519
Coefficient of variation (CV)0.34921087
Kurtosis-0.44006528
Mean0.49688889
Median Absolute Deviation (MAD)0.12
Skewness-0.062522354
Sum232.544
Variance0.030108844
MonotonicityNot monotonic
2025-12-11T20:33:45.647094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.513
 
2.8%
0.5887
 
1.5%
0.6086
 
1.3%
0.256
 
1.3%
0.4576
 
1.3%
0.65
 
1.1%
0.6255
 
1.1%
0.6034
 
0.9%
0.5434
 
0.9%
0.4554
 
0.9%
Other values (276)408
87.2%
ValueCountFrequency (%)
01
0.2%
0.11
0.2%
0.1182
0.4%
0.1251
0.2%
0.1361
0.2%
0.1371
0.2%
0.151
0.2%
0.1541
0.2%
0.1581
0.2%
0.181
0.2%
ValueCountFrequency (%)
11
0.2%
0.9061
0.2%
0.8711
0.2%
0.8611
0.2%
0.861
0.2%
0.8512
0.4%
0.8461
0.2%
0.8411
0.2%
0.8331
0.2%
0.8291
0.2%

MIN_base
Real number (ℝ)

High correlation 

Distinct247
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.371581
Minimum1.8
Maximum37.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:45.706321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile6.27
Q114.6
median21.35
Q328.6
95-th percentile35
Maximum37.7
Range35.9
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.9228902
Coefficient of variation (CV)0.41751194
Kurtosis-0.96762719
Mean21.371581
Median Absolute Deviation (MAD)7.05
Skewness-0.13805728
Sum10001.9
Variance79.61797
MonotonicityNot monotonic
2025-12-11T20:33:45.779993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.87
 
1.5%
24.35
 
1.1%
15.65
 
1.1%
12.45
 
1.1%
155
 
1.1%
25.95
 
1.1%
21.25
 
1.1%
17.64
 
0.9%
14.64
 
0.9%
27.14
 
0.9%
Other values (237)419
89.5%
ValueCountFrequency (%)
1.81
 
0.2%
2.41
 
0.2%
2.51
 
0.2%
3.11
 
0.2%
3.61
 
0.2%
3.92
0.4%
4.43
0.6%
4.52
0.4%
4.61
 
0.2%
51
 
0.2%
ValueCountFrequency (%)
37.71
 
0.2%
37.61
 
0.2%
37.31
 
0.2%
371
 
0.2%
36.71
 
0.2%
36.61
 
0.2%
36.51
 
0.2%
36.41
 
0.2%
36.31
 
0.2%
36.14
0.9%

FGM_base
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6811966
Minimum0
Maximum11.8
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:45.861784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8
Q11.9
median3.2
Q35
95-th percentile8.4
Maximum11.8
Range11.8
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.3431409
Coefficient of variation (CV)0.63651611
Kurtosis0.18941922
Mean3.6811966
Median Absolute Deviation (MAD)1.5
Skewness0.88291163
Sum1722.8
Variance5.4903095
MonotonicityNot monotonic
2025-12-11T20:33:45.917628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.615
 
3.2%
2.414
 
3.0%
2.514
 
3.0%
0.813
 
2.8%
1.313
 
2.8%
3.213
 
2.8%
1.913
 
2.8%
3.312
 
2.6%
2.311
 
2.4%
1.410
 
2.1%
Other values (85)340
72.6%
ValueCountFrequency (%)
01
 
0.2%
0.22
 
0.4%
0.33
 
0.6%
0.42
 
0.4%
0.53
 
0.6%
0.64
 
0.9%
0.76
1.3%
0.813
2.8%
0.94
 
0.9%
13
 
0.6%
ValueCountFrequency (%)
11.81
 
0.2%
11.31
 
0.2%
11.21
 
0.2%
9.81
 
0.2%
9.61
 
0.2%
9.51
 
0.2%
9.31
 
0.2%
9.24
0.9%
9.11
 
0.2%
93
0.6%

FGA_base
Real number (ℝ)

High correlation 

Distinct168
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9113248
Minimum0
Maximum21.8
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:45.978097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.735
Q14
median7
Q310.6
95-th percentile17.965
Maximum21.8
Range21.8
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation4.8859432
Coefficient of variation (CV)0.6175885
Kurtosis-0.099309879
Mean7.9113248
Median Absolute Deviation (MAD)3.2
Skewness0.81744696
Sum3702.5
Variance23.872441
MonotonicityNot monotonic
2025-12-11T20:33:46.036614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.310
 
2.1%
7.69
 
1.9%
4.88
 
1.7%
3.28
 
1.7%
4.67
 
1.5%
57
 
1.5%
7.17
 
1.5%
5.17
 
1.5%
7.56
 
1.3%
5.96
 
1.3%
Other values (158)393
84.0%
ValueCountFrequency (%)
01
 
0.2%
0.31
 
0.2%
0.91
 
0.2%
1.12
 
0.4%
1.24
0.9%
1.34
0.9%
1.42
 
0.4%
1.52
 
0.4%
1.65
1.1%
1.72
 
0.4%
ValueCountFrequency (%)
21.81
0.2%
21.31
0.2%
211
0.2%
20.81
0.2%
20.51
0.2%
20.41
0.2%
20.31
0.2%
19.81
0.2%
19.71
0.2%
19.51
0.2%

FG_PCT_base
Real number (ℝ)

High correlation 

Distinct214
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46324359
Minimum0
Maximum0.798
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:46.091109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.35735
Q10.421
median0.454
Q30.49725
95-th percentile0.6249
Maximum0.798
Range0.798
Interquartile range (IQR)0.07625

Descriptive statistics

Standard deviation0.081780641
Coefficient of variation (CV)0.17653918
Kurtosis3.5693376
Mean0.46324359
Median Absolute Deviation (MAD)0.0375
Skewness0.29493209
Sum216.798
Variance0.0066880733
MonotonicityNot monotonic
2025-12-11T20:33:46.153807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4488
 
1.7%
0.4588
 
1.7%
0.4777
 
1.5%
0.4067
 
1.5%
0.56
 
1.3%
0.4296
 
1.3%
0.4386
 
1.3%
0.3916
 
1.3%
0.4245
 
1.1%
0.4445
 
1.1%
Other values (204)404
86.3%
ValueCountFrequency (%)
01
0.2%
0.1921
0.2%
0.2221
0.2%
0.2451
0.2%
0.2611
0.2%
0.2861
0.2%
0.2891
0.2%
0.2921
0.2%
0.32
0.4%
0.3131
0.2%
ValueCountFrequency (%)
0.7981
0.2%
0.7221
0.2%
0.7061
0.2%
0.7031
0.2%
0.7022
0.4%
0.6922
0.4%
0.6891
0.2%
0.6771
0.2%
0.6691
0.2%
0.6681
0.2%

FG3M
Real number (ℝ)

High correlation  Zeros 

Distinct40
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1858974
Minimum0
Maximum4.4
Zeros46
Zeros (%)9.8%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:46.206079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4
median1.1
Q31.8
95-th percentile2.9
Maximum4.4
Range4.4
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.91600908
Coefficient of variation (CV)0.77241847
Kurtosis0.032346063
Mean1.1858974
Median Absolute Deviation (MAD)0.7
Skewness0.70596654
Sum555
Variance0.83907264
MonotonicityNot monotonic
2025-12-11T20:33:46.260948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
046
 
9.8%
0.328
 
6.0%
1.726
 
5.6%
1.124
 
5.1%
0.223
 
4.9%
1.221
 
4.5%
120
 
4.3%
0.419
 
4.1%
0.517
 
3.6%
0.717
 
3.6%
Other values (30)227
48.5%
ValueCountFrequency (%)
046
9.8%
0.112
 
2.6%
0.223
4.9%
0.328
6.0%
0.419
4.1%
0.517
 
3.6%
0.616
 
3.4%
0.717
 
3.6%
0.816
 
3.4%
0.914
 
3.0%
ValueCountFrequency (%)
4.41
 
0.2%
4.11
 
0.2%
3.92
 
0.4%
3.81
 
0.2%
3.54
0.9%
3.41
 
0.2%
3.32
 
0.4%
3.22
 
0.4%
3.14
0.9%
35
1.1%

FG3A
Real number (ℝ)

High correlation  Zeros 

Distinct95
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3138889
Minimum0
Maximum11.2
Zeros24
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:46.316230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.035
Q11.2
median3.1
Q34.9
95-th percentile7.765
Maximum11.2
Range11.2
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation2.3964283
Coefficient of variation (CV)0.72314686
Kurtosis0.055128799
Mean3.3138889
Median Absolute Deviation (MAD)1.8
Skewness0.66833336
Sum1550.9
Variance5.7428688
MonotonicityNot monotonic
2025-12-11T20:33:46.372087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024
 
5.1%
3.615
 
3.2%
114
 
3.0%
0.113
 
2.8%
3.111
 
2.4%
1.111
 
2.4%
0.610
 
2.1%
0.910
 
2.1%
1.710
 
2.1%
5.910
 
2.1%
Other values (85)340
72.6%
ValueCountFrequency (%)
024
5.1%
0.113
2.8%
0.24
 
0.9%
0.36
 
1.3%
0.46
 
1.3%
0.52
 
0.4%
0.610
2.1%
0.74
 
0.9%
0.87
 
1.5%
0.910
2.1%
ValueCountFrequency (%)
11.22
0.4%
10.91
0.2%
10.31
0.2%
10.11
0.2%
9.61
0.2%
9.31
0.2%
9.21
0.2%
9.12
0.4%
91
0.2%
8.91
0.2%

FG3_PCT
Real number (ℝ)

High correlation  Zeros 

Distinct182
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31764744
Minimum0
Maximum0.667
Zeros37
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:46.430117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.295
median0.345
Q30.38125
95-th percentile0.431
Maximum0.667
Range0.667
Interquartile range (IQR)0.08625

Descriptive statistics

Standard deviation0.11192106
Coefficient of variation (CV)0.35234365
Kurtosis2.8139843
Mean0.31764744
Median Absolute Deviation (MAD)0.0405
Skewness-1.6480451
Sum148.659
Variance0.012526323
MonotonicityNot monotonic
2025-12-11T20:33:46.483685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
7.9%
0.3338
 
1.7%
0.47
 
1.5%
0.3656
 
1.3%
0.3636
 
1.3%
0.3536
 
1.3%
0.3976
 
1.3%
0.3686
 
1.3%
0.3116
 
1.3%
0.3246
 
1.3%
Other values (172)374
79.9%
ValueCountFrequency (%)
037
7.9%
0.0591
 
0.2%
0.1071
 
0.2%
0.151
 
0.2%
0.1541
 
0.2%
0.1561
 
0.2%
0.1581
 
0.2%
0.1761
 
0.2%
0.1881
 
0.2%
0.23
 
0.6%
ValueCountFrequency (%)
0.6671
 
0.2%
0.5651
 
0.2%
0.54
0.9%
0.4561
 
0.2%
0.4471
 
0.2%
0.4462
0.4%
0.4392
0.4%
0.4382
0.4%
0.4371
 
0.2%
0.4362
0.4%

FTM
Real number (ℝ)

High correlation  Zeros 

Distinct59
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5049145
Minimum0
Maximum7.9
Zeros6
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-12-11T20:33:46.544757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.6
median1
Q31.9
95-th percentile4.465
Maximum7.9
Range7.9
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.4023372
Coefficient of variation (CV)0.93183845
Kurtosis3.1263202
Mean1.5049145
Median Absolute Deviation (MAD)0.6
Skewness1.7363551
Sum704.3
Variance1.9665497
MonotonicityNot monotonic
2025-12-11T20:33:46.596520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.834
 
7.3%
0.633
 
7.1%
0.728
 
6.0%
0.427
 
5.8%
1.125
 
5.3%
0.524
 
5.1%
0.323
 
4.9%
1.421
 
4.5%
0.219
 
4.1%
116
 
3.4%
Other values (49)218
46.6%
ValueCountFrequency (%)
06
 
1.3%
0.113
 
2.8%
0.219
4.1%
0.323
4.9%
0.427
5.8%
0.524
5.1%
0.633
7.1%
0.728
6.0%
0.834
7.3%
0.912
 
2.6%
ValueCountFrequency (%)
7.91
0.2%
7.81
0.2%
6.52
0.4%
6.41
0.2%
6.22
0.4%
6.11
0.2%
61
0.2%
5.81
0.2%
5.71
0.2%
5.61
0.2%

PLAYER_NAME
Text

Unique 

Distinct468
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size32.2 KiB
2025-12-11T20:33:46.784859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length21
Mean length13.15812
Min length7

Characters and Unicode

Total characters6158
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique468 ?
Unique (%)100.0%

Sample

1st rowAaron Gordon
2nd rowAaron Holiday
3rd rowAaron Nesmith
4th rowAaron Wiggins
5th rowAdem Bona
ValueCountFrequency (%)
jr20
 
2.1%
williams11
 
1.1%
jalen11
 
1.1%
jordan8
 
0.8%
jones8
 
0.8%
johnson7
 
0.7%
green6
 
0.6%
josh6
 
0.6%
isaiah5
 
0.5%
iii5
 
0.5%
Other values (708)881
91.0%
2025-12-11T20:33:46.994595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a529
 
8.6%
e516
 
8.4%
500
 
8.1%
n474
 
7.7%
r396
 
6.4%
o391
 
6.3%
i357
 
5.8%
l296
 
4.8%
s251
 
4.1%
t187
 
3.0%
Other values (46)2261
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a529
 
8.6%
e516
 
8.4%
500
 
8.1%
n474
 
7.7%
r396
 
6.4%
o391
 
6.3%
i357
 
5.8%
l296
 
4.8%
s251
 
4.1%
t187
 
3.0%
Other values (46)2261
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a529
 
8.6%
e516
 
8.4%
500
 
8.1%
n474
 
7.7%
r396
 
6.4%
o391
 
6.3%
i357
 
5.8%
l296
 
4.8%
s251
 
4.1%
t187
 
3.0%
Other values (46)2261
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a529
 
8.6%
e516
 
8.4%
500
 
8.1%
n474
 
7.7%
r396
 
6.4%
o391
 
6.3%
i357
 
5.8%
l296
 
4.8%
s251
 
4.1%
t187
 
3.0%
Other values (46)2261
36.7%
Distinct353
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Memory size28.7 KiB
2025-12-11T20:33:47.144670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length10
Mean length5.3461538
Min length2

Characters and Unicode

Total characters2502
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique289 ?
Unique (%)61.8%

Sample

1st rowAaron
2nd rowAaron
3rd rowAaron
4th rowAaron
5th rowAdem
ValueCountFrequency (%)
jalen11
 
2.4%
jordan7
 
1.5%
josh6
 
1.3%
isaiah5
 
1.1%
kevin5
 
1.1%
brandon4
 
0.9%
aaron4
 
0.9%
cam4
 
0.9%
chris4
 
0.9%
jaden4
 
0.9%
Other values (342)414
88.5%
2025-12-11T20:33:47.533432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a274
 
11.0%
e248
 
9.9%
n226
 
9.0%
o165
 
6.6%
r161
 
6.4%
i147
 
5.9%
l116
 
4.6%
J101
 
4.0%
s88
 
3.5%
y83
 
3.3%
Other values (47)893
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a274
 
11.0%
e248
 
9.9%
n226
 
9.0%
o165
 
6.6%
r161
 
6.4%
i147
 
5.9%
l116
 
4.6%
J101
 
4.0%
s88
 
3.5%
y83
 
3.3%
Other values (47)893
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a274
 
11.0%
e248
 
9.9%
n226
 
9.0%
o165
 
6.6%
r161
 
6.4%
i147
 
5.9%
l116
 
4.6%
J101
 
4.0%
s88
 
3.5%
y83
 
3.3%
Other values (47)893
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a274
 
11.0%
e248
 
9.9%
n226
 
9.0%
o165
 
6.6%
r161
 
6.4%
i147
 
5.9%
l116
 
4.6%
J101
 
4.0%
s88
 
3.5%
y83
 
3.3%
Other values (47)893
35.7%

TEAM_ABBREVIATION_base
Categorical

High correlation  Uniform 

Distinct30
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
PHI
 
18
WAS
 
17
POR
 
17
CHI
 
17
NOP
 
17
Other values (25)
382 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1404
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEN
2nd rowHOU
3rd rowIND
4th rowOKC
5th rowPHI

Common Values

ValueCountFrequency (%)
PHI18
 
3.8%
WAS17
 
3.6%
POR17
 
3.6%
CHI17
 
3.6%
NOP17
 
3.6%
MIN17
 
3.6%
LAL16
 
3.4%
LAC16
 
3.4%
MIA16
 
3.4%
TOR16
 
3.4%
Other values (20)301
64.3%

Length

2025-12-11T20:33:47.578052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
phi18
 
3.8%
was17
 
3.6%
por17
 
3.6%
chi17
 
3.6%
nop17
 
3.6%
min17
 
3.6%
lal16
 
3.4%
lac16
 
3.4%
mia16
 
3.4%
tor16
 
3.4%
Other values (20)301
64.3%

Most occurring characters

ValueCountFrequency (%)
A156
 
11.1%
L122
 
8.7%
O109
 
7.8%
I100
 
7.1%
N95
 
6.8%
C93
 
6.6%
S90
 
6.4%
H82
 
5.8%
M81
 
5.8%
P67
 
4.8%
Other values (11)409
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A156
 
11.1%
L122
 
8.7%
O109
 
7.8%
I100
 
7.1%
N95
 
6.8%
C93
 
6.6%
S90
 
6.4%
H82
 
5.8%
M81
 
5.8%
P67
 
4.8%
Other values (11)409
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A156
 
11.1%
L122
 
8.7%
O109
 
7.8%
I100
 
7.1%
N95
 
6.8%
C93
 
6.6%
S90
 
6.4%
H82
 
5.8%
M81
 
5.8%
P67
 
4.8%
Other values (11)409
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A156
 
11.1%
L122
 
8.7%
O109
 
7.8%
I100
 
7.1%
N95
 
6.8%
C93
 
6.6%
S90
 
6.4%
H82
 
5.8%
M81
 
5.8%
P67
 
4.8%
Other values (11)409
29.1%
Distinct353
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Memory size28.7 KiB
2025-12-11T20:33:47.731341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length10
Mean length5.3461538
Min length2

Characters and Unicode

Total characters2502
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique289 ?
Unique (%)61.8%

Sample

1st rowAaron
2nd rowAaron
3rd rowAaron
4th rowAaron
5th rowAdem
ValueCountFrequency (%)
jalen11
 
2.4%
jordan7
 
1.5%
josh6
 
1.3%
isaiah5
 
1.1%
kevin5
 
1.1%
brandon4
 
0.9%
aaron4
 
0.9%
cam4
 
0.9%
chris4
 
0.9%
jaden4
 
0.9%
Other values (342)414
88.5%
2025-12-11T20:33:47.955178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a274
 
11.0%
e248
 
9.9%
n226
 
9.0%
o165
 
6.6%
r161
 
6.4%
i147
 
5.9%
l116
 
4.6%
J101
 
4.0%
s88
 
3.5%
y83
 
3.3%
Other values (47)893
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2502
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a274
 
11.0%
e248
 
9.9%
n226
 
9.0%
o165
 
6.6%
r161
 
6.4%
i147
 
5.9%
l116
 
4.6%
J101
 
4.0%
s88
 
3.5%
y83
 
3.3%
Other values (47)893
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2502
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a274
 
11.0%
e248
 
9.9%
n226
 
9.0%
o165
 
6.6%
r161
 
6.4%
i147
 
5.9%
l116
 
4.6%
J101
 
4.0%
s88
 
3.5%
y83
 
3.3%
Other values (47)893
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2502
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a274
 
11.0%
e248
 
9.9%
n226
 
9.0%
o165
 
6.6%
r161
 
6.4%
i147
 
5.9%
l116
 
4.6%
J101
 
4.0%
s88
 
3.5%
y83
 
3.3%
Other values (47)893
35.7%

TEAM_ABBREVIATION_adv
Categorical

High correlation  Uniform 

Distinct30
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size27.6 KiB
PHI
 
18
WAS
 
17
POR
 
17
CHI
 
17
NOP
 
17
Other values (25)
382 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1404
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEN
2nd rowHOU
3rd rowIND
4th rowOKC
5th rowPHI

Common Values

ValueCountFrequency (%)
PHI18
 
3.8%
WAS17
 
3.6%
POR17
 
3.6%
CHI17
 
3.6%
NOP17
 
3.6%
MIN17
 
3.6%
LAL16
 
3.4%
LAC16
 
3.4%
MIA16
 
3.4%
TOR16
 
3.4%
Other values (20)301
64.3%

Length

2025-12-11T20:33:47.994657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
phi18
 
3.8%
was17
 
3.6%
por17
 
3.6%
chi17
 
3.6%
nop17
 
3.6%
min17
 
3.6%
lal16
 
3.4%
lac16
 
3.4%
mia16
 
3.4%
tor16
 
3.4%
Other values (20)301
64.3%

Most occurring characters

ValueCountFrequency (%)
A156
 
11.1%
L122
 
8.7%
O109
 
7.8%
I100
 
7.1%
N95
 
6.8%
C93
 
6.6%
S90
 
6.4%
H82
 
5.8%
M81
 
5.8%
P67
 
4.8%
Other values (11)409
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A156
 
11.1%
L122
 
8.7%
O109
 
7.8%
I100
 
7.1%
N95
 
6.8%
C93
 
6.6%
S90
 
6.4%
H82
 
5.8%
M81
 
5.8%
P67
 
4.8%
Other values (11)409
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A156
 
11.1%
L122
 
8.7%
O109
 
7.8%
I100
 
7.1%
N95
 
6.8%
C93
 
6.6%
S90
 
6.4%
H82
 
5.8%
M81
 
5.8%
P67
 
4.8%
Other values (11)409
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A156
 
11.1%
L122
 
8.7%
O109
 
7.8%
I100
 
7.1%
N95
 
6.8%
C93
 
6.6%
S90
 
6.4%
H82
 
5.8%
M81
 
5.8%
P67
 
4.8%
Other values (11)409
29.1%

Interactions

2025-12-11T20:33:43.790439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:33.407624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-11T20:33:37.978088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:38.659342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:39.352339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-11T20:33:39.161392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:39.857547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:40.681531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:41.427856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:42.092107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:42.744726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:43.594893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:44.378267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:34.022521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:34.756281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:35.584866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:36.289969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:36.975069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:37.846770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:38.524357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:39.209332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:39.903569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:40.722468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:41.474520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:42.131885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:42.788289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:43.640481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:44.427962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:34.069521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:34.800686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:35.623789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:36.333225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:37.017551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:37.889009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:38.568877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:39.254754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:39.944831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:40.767275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:41.522554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:42.172184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:42.829550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:43.687329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:44.477126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:34.113787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:35.010113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:35.671931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:36.383029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:37.061705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:37.934227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:38.614815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:39.304801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:39.993097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:40.811283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:41.570340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:42.219121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:42.873798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-11T20:33:43.738971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-11T20:33:48.046322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
PLAYER_IDTEAM_ID_baseAGE_baseGP_baseW_baseL_baseW_PCT_baseMIN_baseFGM_baseFGA_baseFG_PCT_baseFG3MFG3AFG3_PCTFTM
PLAYER_ID1.0000.045-0.7220.0200.0020.029-0.002-0.090-0.083-0.0760.015-0.047-0.025-0.012-0.134
TEAM_ID_base0.0451.000-0.133-0.038-0.2520.227-0.3460.0080.0060.025-0.052-0.0040.013-0.0120.014
AGE_base-0.722-0.1331.0000.0100.093-0.0890.0800.1040.0670.0560.0080.1120.0780.0910.063
GP_base0.020-0.0380.0101.0000.7790.7020.1310.5240.3920.3740.1790.3530.3390.1770.249
W_base0.002-0.2520.0930.7791.0000.1000.6690.3870.3260.2880.1800.2890.2570.1500.204
L_base0.0290.227-0.0890.7020.1001.000-0.5520.3930.2530.2670.0790.2310.2450.1110.162
W_PCT_base-0.002-0.3460.0800.1310.669-0.5521.000-0.0470.020-0.0190.0740.013-0.0180.0090.008
MIN_base-0.0900.0080.1040.5240.3870.393-0.0471.0000.8790.8860.1250.6790.6960.2200.721
FGM_base-0.0830.0060.0670.3920.3260.2530.0200.8791.0000.9760.2080.6430.6570.1830.870
FGA_base-0.0760.0250.0560.3740.2880.267-0.0190.8860.9761.0000.0400.7470.7740.2540.864
FG_PCT_base0.015-0.0520.0080.1790.1800.0790.0740.1250.2080.0401.000-0.251-0.303-0.2390.126
FG3M-0.047-0.0040.1120.3530.2890.2310.0130.6790.6430.747-0.2511.0000.9860.5430.505
FG3A-0.0250.0130.0780.3390.2570.245-0.0180.6960.6570.774-0.3030.9861.0000.4990.536
FG3_PCT-0.012-0.0120.0910.1770.1500.1110.0090.2200.1830.254-0.2390.5430.4991.0000.090
FTM-0.1340.0140.0630.2490.2040.1620.0080.7210.8700.8640.1260.5050.5360.0901.000
2025-12-11T20:33:48.137305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
PLAYER_IDTEAM_ID_baseAGE_baseGP_baseW_baseL_baseW_PCT_baseMIN_baseFGM_baseFGA_baseFG_PCT_baseFG3MFG3AFG3_PCTFTM
PLAYER_ID1.0000.128-0.893-0.065-0.1290.043-0.076-0.258-0.224-0.211-0.111-0.169-0.150-0.145-0.191
TEAM_ID_base0.1281.000-0.151-0.031-0.2540.219-0.3600.0070.0230.037-0.0540.0080.025-0.0390.056
AGE_base-0.893-0.1511.0000.0360.126-0.0760.1030.1240.0770.0680.0690.1250.0930.1900.024
GP_base-0.065-0.0310.0361.0000.7840.6800.1490.5110.4640.4480.1680.3700.3660.2170.349
W_base-0.129-0.2540.1260.7841.0000.1480.6710.3800.3420.3110.2050.2850.2640.2350.213
L_base0.0430.219-0.0760.6800.1481.000-0.5390.3850.3550.3600.0630.2550.2700.0800.333
W_PCT_base-0.076-0.3600.1030.1490.671-0.5391.000-0.029-0.026-0.0560.1130.002-0.0220.098-0.099
MIN_base-0.2580.0070.1240.5110.3800.385-0.0291.0000.9240.9300.1420.6790.7000.2270.804
FGM_base-0.2240.0230.0770.4640.3420.355-0.0260.9241.0000.9760.2700.6500.6640.2270.870
FGA_base-0.2110.0370.0680.4480.3110.360-0.0560.9300.9761.0000.0840.7500.7750.2620.854
FG_PCT_base-0.111-0.0540.0690.1680.2050.0630.1130.1420.2700.0841.000-0.265-0.3210.0480.221
FG3M-0.1690.0080.1250.3700.2850.2550.0020.6790.6500.750-0.2651.0000.9870.6190.481
FG3A-0.1500.0250.0930.3660.2640.270-0.0220.7000.6640.775-0.3210.9871.0000.5150.514
FG3_PCT-0.145-0.0390.1900.2170.2350.0800.0980.2270.2270.2620.0480.6190.5151.0000.064
FTM-0.1910.0560.0240.3490.2130.333-0.0990.8040.8700.8540.2210.4810.5140.0641.000
2025-12-11T20:33:48.231240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
PLAYER_IDTEAM_ID_baseAGE_baseGP_baseW_baseL_baseW_PCT_baseMIN_baseFGM_baseFGA_baseFG_PCT_baseFG3MFG3AFG3_PCTFTM
PLAYER_ID1.0000.085-0.744-0.045-0.0850.029-0.050-0.174-0.155-0.144-0.078-0.114-0.099-0.099-0.133
TEAM_ID_base0.0851.000-0.106-0.021-0.1750.152-0.2510.0050.0160.025-0.0360.0060.016-0.0270.040
AGE_base-0.744-0.1061.0000.0250.088-0.0530.0700.0860.0560.0480.0500.0860.0620.1330.017
GP_base-0.045-0.0210.0251.0000.6000.5070.1000.3590.3230.3090.1140.2570.2490.1510.244
W_base-0.085-0.1750.0880.6001.0000.0900.5150.2600.2350.2120.1410.2000.1820.1660.148
L_base0.0290.152-0.0530.5070.0901.000-0.4070.2660.2430.2470.0410.1760.1840.0510.227
W_PCT_base-0.050-0.2510.0700.1000.515-0.4071.000-0.018-0.015-0.0360.0790.002-0.0140.067-0.065
MIN_base-0.1740.0050.0860.3590.2600.266-0.0181.0000.7650.7690.1020.5070.5220.1540.621
FGM_base-0.1550.0160.0560.3230.2350.243-0.0150.7651.0000.8790.1890.4920.5010.1550.703
FGA_base-0.1440.0250.0480.3090.2120.247-0.0360.7690.8791.0000.0580.5760.5990.1790.681
FG_PCT_base-0.078-0.0360.0500.1140.1410.0410.0790.1020.1890.0581.000-0.182-0.2190.0570.150
FG3M-0.1140.0060.0860.2570.2000.1760.0020.5070.4920.576-0.1821.0000.9200.4720.348
FG3A-0.0990.0160.0620.2490.1820.184-0.0140.5220.5010.599-0.2190.9201.0000.3780.370
FG3_PCT-0.099-0.0270.1330.1510.1660.0510.0670.1540.1550.1790.0570.4720.3781.0000.043
FTM-0.1330.0400.0170.2440.1480.227-0.0650.6210.7030.6810.1500.3480.3700.0431.000
2025-12-11T20:33:48.324685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
PLAYER_IDTEAM_ID_baseAGE_baseGP_baseW_baseL_baseW_PCT_baseMIN_baseFGM_baseFGA_baseFG_PCT_baseFG3MFG3AFG3_PCTFTMTEAM_ABBREVIATION_baseTEAM_ABBREVIATION_adv
PLAYER_ID1.0000.0000.7720.0000.0000.0440.0000.1810.1370.1150.0000.0000.0000.0000.1470.2240.224
TEAM_ID_base0.0001.0000.1240.0000.5350.4240.7740.2390.0000.0000.0000.1820.0000.0000.0131.0001.000
AGE_base0.7720.1241.0000.0000.1330.2350.2180.1950.2800.2050.1320.1630.0000.3030.0000.0000.000
GP_base0.0000.0000.0001.0000.7960.7710.3580.5890.4610.4820.3620.3200.3180.4090.2950.2850.285
W_base0.0000.5350.1330.7961.0000.6260.7750.4400.4170.3040.2330.2970.2790.2650.2820.6900.690
L_base0.0440.4240.2350.7710.6261.0000.6990.5530.4860.4820.2800.2770.3670.1410.3590.5780.578
W_PCT_base0.0000.7740.2180.3580.7750.6991.0000.3110.0000.0000.3650.0000.0000.1970.0000.8900.890
MIN_base0.1810.2390.1950.5890.4400.5530.3111.0000.8840.8850.2880.6960.7310.4550.7500.2800.280
FGM_base0.1370.0000.2800.4610.4170.4860.0000.8841.0000.9460.2970.7040.7160.2660.8500.0000.000
FGA_base0.1150.0000.2050.4820.3040.4820.0000.8850.9461.0000.2480.7700.8200.3970.8290.0000.000
FG_PCT_base0.0000.0000.1320.3620.2330.2800.3650.2880.2970.2481.0000.3340.3840.5500.0000.0000.000
FG3M0.0000.1820.1630.3200.2970.2770.0000.6960.7040.7700.3341.0000.9630.6420.6200.0000.000
FG3A0.0000.0000.0000.3180.2790.3670.0000.7310.7160.8200.3840.9631.0000.6250.6850.0000.000
FG3_PCT0.0000.0000.3030.4090.2650.1410.1970.4550.2660.3970.5500.6420.6251.0000.0000.0000.000
FTM0.1470.0130.0000.2950.2820.3590.0000.7500.8500.8290.0000.6200.6850.0001.0000.0720.072
TEAM_ABBREVIATION_base0.2241.0000.0000.2850.6900.5780.8900.2800.0000.0000.0000.0000.0000.0000.0721.0001.000
TEAM_ABBREVIATION_adv0.2241.0000.0000.2850.6900.5780.8900.2800.0000.0000.0000.0000.0000.0000.0721.0001.000
2025-12-11T20:33:48.411389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
TEAM_ABBREVIATION_advTEAM_ABBREVIATION_base
TEAM_ABBREVIATION_adv1.0001.000
TEAM_ABBREVIATION_base1.0001.000
2025-12-11T20:33:48.471840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AGE_baseFG3AFG3MFG3_PCTFGA_baseFGM_baseFG_PCT_baseFTMGP_baseL_baseMIN_basePLAYER_IDTEAM_ABBREVIATION_advTEAM_ABBREVIATION_baseTEAM_ID_baseW_PCT_baseW_base
AGE_base1.0000.0930.1250.1900.0680.0770.0690.0240.036-0.0760.124-0.8930.0000.000-0.1510.1030.126
FG3A0.0931.0000.9870.5150.7750.664-0.3210.5140.3660.2700.700-0.1500.0000.0000.025-0.0220.264
FG3M0.1250.9871.0000.6190.7500.650-0.2650.4810.3700.2550.679-0.1690.0000.0000.0080.0020.285
FG3_PCT0.1900.5150.6191.0000.2620.2270.0480.0640.2170.0800.227-0.1450.0000.000-0.0390.0980.235
FGA_base0.0680.7750.7500.2621.0000.9760.0840.8540.4480.3600.930-0.2110.0000.0000.037-0.0560.311
FGM_base0.0770.6640.6500.2270.9761.0000.2700.8700.4640.3550.924-0.2240.0000.0000.023-0.0260.342
FG_PCT_base0.069-0.321-0.2650.0480.0840.2701.0000.2210.1680.0630.142-0.1110.0000.000-0.0540.1130.205
FTM0.0240.5140.4810.0640.8540.8700.2211.0000.3490.3330.804-0.1910.0190.0190.056-0.0990.213
GP_base0.0360.3660.3700.2170.4480.4640.1680.3491.0000.6800.511-0.0650.0920.092-0.0310.1490.784
L_base-0.0760.2700.2550.0800.3600.3550.0630.3330.6801.0000.3850.0430.2160.2160.219-0.5390.148
MIN_base0.1240.7000.6790.2270.9300.9240.1420.8040.5110.3851.000-0.2580.0900.0900.007-0.0290.380
PLAYER_ID-0.893-0.150-0.169-0.145-0.211-0.224-0.111-0.191-0.0650.043-0.2581.0000.1020.1020.128-0.076-0.129
TEAM_ABBREVIATION_adv0.0000.0000.0000.0000.0000.0000.0000.0190.0920.2160.0900.1021.0001.0000.9780.5110.286
TEAM_ABBREVIATION_base0.0000.0000.0000.0000.0000.0000.0000.0190.0920.2160.0900.1021.0001.0000.9780.5110.286
TEAM_ID_base-0.1510.0250.008-0.0390.0370.023-0.0540.056-0.0310.2190.0070.1280.9780.9781.000-0.360-0.254
W_PCT_base0.103-0.0220.0020.098-0.056-0.0260.113-0.0990.149-0.539-0.029-0.0760.5110.511-0.3601.0000.671
W_base0.1260.2640.2850.2350.3110.3420.2050.2130.7840.1480.380-0.1290.2860.286-0.2540.6711.000

Missing values

2025-12-11T20:33:44.544371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-11T20:33:44.631944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PLAYER_IDTEAM_ID_baseAGE_baseGP_baseW_baseL_baseW_PCT_baseMIN_baseFGM_baseFGA_baseFG_PCT_baseFG3MFG3AFG3_PCTFTMPLAYER_NAMENICKNAME_baseTEAM_ABBREVIATION_baseNICKNAME_advTEAM_ABBREVIATION_adv
0203932.01.610613e+0929.051.033.018.00.64728.45.29.70.5311.53.40.4362.8Aaron GordonAaronDENAaronDEN
11628988.01.610613e+0928.062.039.023.00.62912.81.94.30.4371.22.90.3980.5Aaron HolidayAaronHOUAaronHOU
21630174.01.610613e+0925.045.029.016.00.64424.94.38.40.5071.94.30.4311.6Aaron NesmithAaronINDAaronIND
31630598.01.610613e+0926.076.062.014.00.81622.94.79.60.4881.74.50.3831.0Aaron WigginsAaronOKCAaronOKC
41641737.01.610613e+0922.058.012.046.00.20715.62.33.30.7030.00.00.0001.2Adem BonaAdemPHIAdemPHI
51642349.01.610613e+0923.036.031.05.00.86116.62.55.10.4950.61.70.3830.8Ajay MitchellAjayOKCAjayOKC
61631260.01.610613e+0925.073.044.029.00.60322.72.55.80.4292.15.00.4270.3AJ GreenAJMILAJMIL
71642358.01.610613e+0920.029.08.021.00.27622.02.87.30.3850.83.10.2671.1AJ JohnsonAJWASAJWAS
81630639.01.610613e+0924.026.014.012.00.53818.73.17.30.4211.33.90.3271.7A.J. LawsonA.J.TORA.J.TOR
9202692.01.610613e+0933.049.026.023.00.53117.62.35.50.4241.84.20.4250.8Alec BurksAlecMIAAlecMIA
PLAYER_IDTEAM_ID_baseAGE_baseGP_baseW_baseL_baseW_PCT_baseMIN_baseFGM_baseFGA_baseFG_PCT_baseFG3MFG3AFG3_PCTFTMPLAYER_NAMENICKNAME_baseTEAM_ABBREVIATION_baseNICKNAME_advTEAM_ABBREVIATION_adv
4581631111.01.610613e+0923.036.011.025.00.30613.91.63.50.4680.41.10.3410.5Wendell Moore Jr.WendellCHAWendellCHA
4591630214.01.610613e+0926.033.026.07.00.7887.00.41.60.2450.21.00.1560.1Xavier TillmanXavierBOSXavierBOS
4601642274.01.610613e+0921.073.019.054.00.26026.83.76.70.5470.00.00.0001.7Yves MissiYvesNOPYvesNOP
4611642258.01.610613e+0920.075.038.037.00.50724.64.810.40.4581.64.60.3551.4Zaccharie RisacherZaccharieATLZaccharieATL
4621628380.01.610613e+0927.064.034.030.00.53115.32.44.70.5070.51.70.3021.1Zach CollinsZachCHIZachCHI
4631641744.01.610613e+0923.066.035.031.00.53021.53.86.60.5800.30.80.3461.4Zach EdeyZachMEMZachMEM
464203897.01.610613e+0930.074.032.042.00.43235.28.416.50.5113.27.20.4463.2Zach LaVineZachSACZachSAC
4651630192.01.610613e+0924.057.036.021.00.63210.71.22.50.4960.30.90.3270.5Zeke NnajiZekeDENZekeDEN
4661630533.01.610613e+0923.063.022.041.00.34924.53.48.30.4121.64.80.3411.6Ziaire WilliamsZiaireBKNZiaireBKN
4671629627.01.610613e+0924.030.010.020.00.33328.69.616.90.5670.10.40.2315.3Zion WilliamsonZionNOPZionNOP
PLAYER_IDTEAM_ID_baseAGE_baseGP_baseW_baseL_baseW_PCT_baseMIN_baseFGM_baseFGA_baseFG_PCT_baseFG3MFG3AFG3_PCTFTMPLAYER_NAMENICKNAME_baseTEAM_ABBREVIATION_baseNICKNAME_advTEAM_ABBREVIATION_adv
321631248.01.610613e+0924.031.027.04.00.87112.41.33.50.3550.82.60.3170.3Baylor ScheiermanBaylorBOSBaylorBOS
1801631222.01.610613e+0923.066.040.026.00.60620.42.45.10.4750.92.20.4231.2Jake LaRaviaJakeSACJakeSAC
4331629027.01.610613e+0926.076.036.040.00.47436.07.418.10.4112.98.40.3406.5Trae YoungTraeATLTraeATL
2981626204.01.610613e+0932.024.010.014.00.41719.33.36.50.5161.43.20.4470.4Larry Nance Jr.LarryATLLarryATL
3961642346.01.610613e+0922.074.032.042.00.43219.12.86.40.4301.13.60.3110.3Ryan DunnRyanPHXRyanPHX
391626171.01.610613e+0930.049.030.019.00.61225.45.712.10.4661.33.60.3651.2Bobby PortisBobbyMILBobbyMIL
1851630224.01.610613e+0923.082.052.030.00.63432.97.417.50.4232.98.10.3543.3Jalen GreenJalenHOUJalenHOU
114203471.01.610613e+0931.075.037.038.00.49328.14.511.00.4061.74.90.3422.4Dennis SchroderDennisDETDennisDET
1331631166.01.610613e+0924.09.03.06.00.33328.25.011.30.4411.03.90.2571.1Drew TimmeDrewBKNDrewBKN
1531641711.01.610613e+0921.054.015.039.00.27829.44.912.00.4102.16.00.3502.5Gradey DickGradeyTORGradeyTOR